Results on DomainNet:
Tab. REF  shows the comparison of our proposed framework with state-of-the-art methods and all baselines, as established in [1]}, on the ZSLDG task.
We first report the performance of standalone ZSL approaches such as DEVISE [2]}, ALE [3]} and SPNet [4]} on the ZSLDG task, followed by the performance achieved by coupling these ZSL approaches with standard DG approaches like DANN [5]} and EpiFCR [6]}.
It is worth noting that coupling the standalone ZSL methods with DANN achieves lower performance than the ZSL method alone in the case of ZSLDG, since standard domain alignment methods have been shown to be ineffective on the DomainNet dataset, leading to negative transfer in some cases [7]}. Furthermore, as noted by [1]}, coupling EpiFCR (a standalone DG method) with the standalone ZSL approaches is not straightforward, since it requires careful adaptation that includes re-structuring of the loss terms. In particular, the approach of EpiFCR+SPNet achieves an average accuracy (AVG) of \(20.0\)  over different target domains.
The recently introduced CuMix [1]} approach that targets ZSLDG, employs a curriculum-based mixing policy to generate increasingly complex training samples by mixing up multiple seen domains and categories available during training. The current state-of-art CuMix improves ZSLDG performance over EpiFCR+SPNet, achieving an average accuracy of \(20.7\)  across the target domains. Our approach outperforms CuMix with an absolute gain of \(1.2\%\)  average across domains ( \(\sim 6\%\)  relative increase) and achieves average accuracy of \(21.9\)  across the five target domains, setting a new state of the art. Furthermore, our method achieves consistent gains over CuMix on each of the target domains.
